机器学习在肾细胞癌研究中的应用：挖掘人工智能潜力之“肾”路的前景与陷阱 - Suppr

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Machine learning in renal cell carcinoma research: the promise and pitfalls of 'renal-izing' the potential of artificial intelligence.

作者信息

Khene Zine-Eddine, Kutikov Alexander, Campi Riccardo

机构信息

Department of Urology, Rennes University Hospital, Rennes, France.

Signal and Image Processing Laboratory (LTSI), University of Rennes INSERM, Rennes, France.

出版信息

BJU Int. 2023 Aug;132(2):231-232. doi: 10.1111/bju.16016. Epub 2023 Apr 10.

DOI:10.1111/bju.16016

PMID:37005367

Abstract

摘要

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引用本文的文献

MtDNA copy number enrichment is associated with poor prognosis and eosinophilic morphology in clear cell renal cell carcinoma.

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Machine learning in renal cell carcinoma research: the promise and pitfalls of 'renal-izing' the potential of artificial intelligence.

作者信息

Khene Zine-Eddine, Kutikov Alexander, Campi Riccardo

机构信息

Department of Urology, Rennes University Hospital, Rennes, France.

Signal and Image Processing Laboratory (LTSI), University of Rennes INSERM, Rennes, France.

出版信息

BJU Int. 2023 Aug;132(2):231-232. doi: 10.1111/bju.16016. Epub 2023 Apr 10.

DOI:10.1111/bju.16016

PMID:37005367

Abstract

摘要

相似文献

Machine learning in renal cell carcinoma research: the promise and pitfalls of 'renal-izing' the potential of artificial intelligence.

BJU Int. 2023 Aug;132(2):231-232. doi: 10.1111/bju.16016. Epub 2023 Apr 10.

Reply to: machine learning in renal cell carcinoma research: promise and pitfalls of 'renal-izing' the potential of artificial intelligence.

BJU Int. 2023 Dec;132(6):713-714. doi: 10.1111/bju.16116. Epub 2023 Jul 28.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma.

Urol Clin North Am. 2024 Feb;51(1):47-62. doi: 10.1016/j.ucl.2023.06.002. Epub 2023 Jul 21.

Kidney cancer management 3.0: can artificial intelligence make us better?

Curr Opin Urol. 2021 Jul 1;31(4):409-415. doi: 10.1097/MOU.0000000000000881.

Prediction of the Values of CRP, eGFR, and Hemoglobin in the Follow-Up of Renal Cell Carcinoma Patients after (Cryo)Surgery Using Machine Learning Algorithms.

J Appl Lab Med. 2022 Jun 30;7(4):819-826. doi: 10.1093/jalm/jfab177.

Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.

World J Urol. 2020 Oct;38(10):2329-2347. doi: 10.1007/s00345-019-03000-5. Epub 2019 Nov 5.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

Eur Radiol. 2019 Mar;29(3):1153-1163. doi: 10.1007/s00330-018-5698-2. Epub 2018 Aug 30.

Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms.

J Comput Assist Tomogr. 2023;47(3):376-381. doi: 10.1097/RCT.0000000000001433. Epub 2023 Feb 10.

Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept.

World J Urol. 2021 Sep;39(9):3707-3709. doi: 10.1007/s00345-020-03334-5. Epub 2020 Jul 6.

Radiomics and Artificial Intelligence: Renal Cell Carcinoma.

Urol Clin North Am. 2024 Feb;51(1):35-45. doi: 10.1016/j.ucl.2023.06.007. Epub 2023 Jul 28.

引用本文的文献

MtDNA copy number enrichment is associated with poor prognosis and eosinophilic morphology in clear cell renal cell carcinoma.

Pathol Oncol Res. 2025 Jul 23;31:1612172. doi: 10.3389/pore.2025.1612172. eCollection 2025.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives.

Diagnostics (Basel). 2023 Jul 6;13(13):2294. doi: 10.3390/diagnostics13132294.